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Tech Stack
Tools & technologiesAWSCloudDockerHadoopKubernetesSpark
About the role
Key responsibilities & impact- Collaborate cross-functionally with domain experts, data scientists, and stakeholders to translate business requirements into technical requirements
- Implement automated pipelines for data processing and deep learning or LLM model training, evaluation, and deployment, including Agentic AI
- Design scalable infrastructure for model training and inference in cloud and on-premises environments
- Set up monitoring to enable proactive countermeasures for model degradation
- Ensure data privacy and compliance with healthcare regulations such as GDPR and HIPAA during model development and deployment
Requirements
What you’ll need- Master's degree or PhD in computer science, mathematics, or related fields, OR bachelor's degree with at least 5 years of experience
- Experience creating engineering solutions supporting an AI/ML model lifecycle, including data ingestion, training, evaluation, and deployment
- Familiarity with a cloud provider, preferably AWS
- Proficiency in large-scale data processing technologies such as Hadoop and Spark
- Hands-on experience with scalable model deployment using Docker and Kubernetes
- Maintainable and reusable coding skills, including Git source control, unit and integration testing, and CI/CD concepts
- Must be legally authorized to work in the country of employment without employment visa sponsorship
Benefits
Comp & perks- Competitive pay and benefits
- Programs supporting physical and financial well-being
- Equal opportunity and diversity commitment